Text Sequence Style Transfer Using Dual Encoder-Decoder Models
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Solution Overview
Problem
The high labeling costs and scarcity of sentence pairs with different styles but similar meaning pose a significant challenge in preparing training data for sequence-to-sequence style transfer tasks in natural language generation, limiting the effectiveness of existing methods.
Innovation Solution
A method utilizing two encoder-decoder neural network models and a classifier to train a neural network model for text sequence style transfer, where one encoder-decoder generates an output sequence based on an input sequence and style, and the other predicts the input sequence, with a classifier predicting the style, allowing for updates based on comparisons to align the output with the input sequence and style.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing sequence-to-sequence methods are used for style transfer, then good style transfer results can be achieved, but the labeling costs are very high and training data is scarce
Solution Approach 1:
The patent segments the training data requirement into two parts: (1) abundant parallel sentences with different styles but similar meaning are used to train the first encoder-decoder model, and (2) fewer style-labeled sentences are needed for the second model. This segmentation reduces the overall labeling burden while maintaining style transfer effectiveness.
Solution Approach 2:
The patent introduces an intermediary first encoder-decoder model that translates sentences from one style to another. This intermediary model is trained on abundant parallel data without requiring style labels, and its output is then used by the second model which only needs minimal style-labeled data for training.
2Reliability
If abundant training pairs are used to achieve good results, then model performance improves, but the labeling costs involved in preparing the training data are very high
Solution Approach 1:
The training process is segmented into two phases: Phase 1 trains the first model on abundant parallel sentences that do not require style labeling, and Phase 2 uses this pre-trained model to generate synthetic data that reduces the need for expensive human labeling in Phase 3.
Solution Approach 2:
The first encoder-decoder model is trained in advance on abundant parallel sentences before the actual style transfer training begins. This preliminary action creates a resource that can be reused to generate synthetic training data, reducing the need for expensive manual labeling later.
3Adaptability or versatility
If pairs of sentences with different styles but similar meaning are acquired, then style transfer training is enabled, but these pairs are even more scarce and difficult to acquire
Solution Approach 1:
The first encoder-decoder model serves itself by generating synthetic parallel sentences in different styles from the abundant training data. This self-generated synthetic data then serves as training material for the second model, eliminating the need for manually creating scarce style-paired sentences.
Solution Approach 2:
The patent uses the first encoder-decoder model to copy and transform sentences into different styles, creating synthetic training pairs. These copied and transformed sentences serve as effective training data for the second model, replacing the need for manually collected scarce style-paired sentences.
Data Source
AI summary
A method for text sequence style transfer by two encoder-decoders, including generating, by a first encoder-decoder network model, an output sequence based on a first input sequence and an input sequence style, wherein the output sequence is associated with a second sequence, generating, by a second-encoder decoder network model, a prediction of the first input sequence based on the first input sequence, the output sequence, and a first input sequence style associated with the first input sequence, generating, by a classifier, a prediction of the first input sequence style based on the prediction of the first input sequence, and updating the neural network model based on comparisons between the output sequence and the second sequence, between the prediction of the first input sequence and the first input sequence, and between the prediction of the first input sequence style and the first input sequence style.